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• Companies worldwide are leveraging AI and machine learning to enhance innovation and efficiency, but few report success in using AI to deliver business value.
• Three key stumbling blocks to AI success include high-friction data management, which is difficult to operationalize due to data sprawl and lack of open standards.
• The Databricks Lakehouse Platform, with its data-centric AI approach, helps deliver these capabilities and accelerates time to business value.
• Critic criteria for AI success include a fine-tuned focus on data, an embrace of open standards, and the simplification of processes. High-friction data management is a major challenge, as it limits data lineage and visibility, creates barriers to model versioning, governance, and compliance, and can lead to stale data.
• Featurization is another challenge, as engineers must observe input data and features in real time and share model metrics and logs.
• Pockets of AI applications are another obstacle, as data science and AI/ML are code-heavy and serve primarily niche use cases.
• Businesses often lack skilled staff and frameworks to consistently curate, catalog, and utilize various types of data across the data lifecycle.

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